A typical enterprise computing environment includes multiple heterogeneous and distributed data systems supporting a variety of different enterprise organizations (e.g., departments). For example, many enterprises such as businesses and the like maintain separate database systems to support customer billing, sales, accounting, marketing, inventory, ordering, repairs, procurement, service, etc. Further, many enterprises are the result of a merger of two or more predecessors, each with their own set of heterogeneous and distributed data systems.
Data stored in the heterogeneous database systems of an enterprise is typically used for different business purposes and may be defined according to different local schemas (e.g., defined in different formats, according to different technologies, according to different data models, or according to different business rules). For example, different enterprise database systems may use different local schemas to represent a customer and/or accounts associated with the customer. In addition, because enterprise database systems often serve different business purposes, the relationships between customer and account records, and the data included in the records, may vary and/or may be defined differently across the enterprise database systems.
Maintaining multiple heterogeneous database systems can require a significant amount of work and sophistication, especially for enterprises storing large amounts of data. One area of concern involves the handling of data updates. It is particularly challenging and time consuming to propagate data updates across heterogeneous database systems in a manner that accurately and uniformly reflects customer and account evolutions while also conforming to disparate business rules and data schemas associated with the different departments in the enterprise. Conventional techniques for propagating data updates between heterogeneous data subsystems rely on ad hoc peer-to-peer relationships and communications. In certain cases, significant amounts of manual labor are required to assess peer-to-peer relationships between database systems in an ad hoc fashion. Such techniques can quickly become complicated and time consuming for an enterprise that maintains heterogeneous database systems storing large amounts of data and/or having unique and dynamic relationships with one another.